Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:41:56.936798
Analysis finished2020-08-25 00:42:15.104702
Duration18.17 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.398155629634857e-10
Minimum-2.2736310958862305
Maximum2.4353008270263667
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:15.153373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.273631096
5-th percentile-1.618339622
Q1-0.7449063808
median-0.05385065265
Q30.7453725487
95-th percentile1.681768548
Maximum2.435300827
Range4.708931923
Interquartile range (IQR)1.490278929

Descriptive statistics

Standard deviation0.9999999993
Coefficient of variation (CV)4169871158
Kurtosis-0.7211901833
Mean2.39815563e-10
Median Absolute Deviation (MAD)0.7442420423
Skewness0.07030568375
Sum1.199077815e-07
Variance0.9999999986
2020-08-25T00:42:15.255383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.825194239610.2%
 
0.972988009510.2%
 
0.892887532710.2%
 
1.82466256610.2%
 
-0.863592326610.2%
 
-0.0110124023610.2%
 
-1.71496701210.2%
 
-1.40736484510.2%
 
0.790356993710.2%
 
0.922193646410.2%
 
-1.72249305210.2%
 
-0.697587370910.2%
 
-0.722982227810.2%
 
-0.918058812610.2%
 
-0.45622044810.2%
 
1.44012057810.2%
 
-1.64134693110.2%
 
0.894867360610.2%
 
-0.929349064810.2%
 
0.419116079810.2%
 
-1.30009114710.2%
 
0.00625880109110.2%
 
-1.04171168810.2%
 
-1.29617714910.2%
 
-1.18656015410.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.27363109610.2%
 
-2.09339308710.2%
 
-2.06829142610.2%
 
-1.99057447910.2%
 
-1.98884236810.2%
 
-1.9547668710.2%
 
-1.95090615710.2%
 
-1.93807864210.2%
 
-1.93103313410.2%
 
-1.91738784310.2%
 
ValueCountFrequency (%) 
2.43530082710.2%
 
2.27556991610.2%
 
2.20964002610.2%
 
2.18372869510.2%
 
2.07440495510.2%
 
2.07200074210.2%
 
2.06074261710.2%
 
1.97930097610.2%
 
1.94246065610.2%
 
1.91456079510.2%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.8011778593063354e-09
Minimum-1.6405104398727417
Maximum1.7997990846633911
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:15.368580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.64051044
5-th percentile-1.479510528
Q1-0.8578256667
median-0.04767413996
Q30.87701267
95-th percentile1.58272239
Maximum1.799799085
Range3.440309525
Interquartile range (IQR)1.734838337

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-555192256.7
Kurtosis-1.215095497
Mean-1.801177859e-09
Median Absolute Deviation (MAD)0.8468387332
Skewness0.1183396257
Sum-9.005889297e-07
Variance1.000000001
2020-08-25T00:42:15.473526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.31154835210.2%
 
1.36003422710.2%
 
0.0164539217910.2%
 
0.355797827210.2%
 
1.34831845810.2%
 
1.50259208710.2%
 
0.924149274810.2%
 
-1.57681512810.2%
 
-0.813799321710.2%
 
0.445962578110.2%
 
0.0361530967110.2%
 
0.762044191410.2%
 
-1.61393594710.2%
 
0.635042309810.2%
 
-1.00261092210.2%
 
1.36980330910.2%
 
-1.31126594510.2%
 
-1.26498293910.2%
 
0.0551357418310.2%
 
-1.50127947310.2%
 
-0.00400019809610.2%
 
-0.849948942710.2%
 
-1.05872631110.2%
 
0.634132087210.2%
 
-1.31894350110.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.6405104410.2%
 
-1.63896107710.2%
 
-1.63408660910.2%
 
-1.6167322410.2%
 
-1.61393594710.2%
 
-1.60573291810.2%
 
-1.60307514710.2%
 
-1.57681512810.2%
 
-1.5612109910.2%
 
-1.5610753310.2%
 
ValueCountFrequency (%) 
1.79979908510.2%
 
1.79553651810.2%
 
1.79090774110.2%
 
1.78837990810.2%
 
1.78360092610.2%
 
1.78266525310.2%
 
1.77738475810.2%
 
1.77024197610.2%
 
1.75203120710.2%
 
1.74579238910.2%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.95521817356348e-10
Minimum-2.1268887519836426
Maximum3.720251083374024
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:15.585614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.126888752
5-th percentile-1.323527122
Q1-0.7108676434
median-0.2039072514
Q30.6078371853
95-th percentile2.002986348
Maximum3.720251083
Range5.847139835
Interquartile range (IQR)1.318704829

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)2018074617
Kurtosis0.3851783006
Mean4.955218174e-10
Median Absolute Deviation (MAD)0.6260196418
Skewness0.7972004299
Sum2.477609087e-07
Variance1.000000004
2020-08-25T00:42:15.862038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.2427227510.2%
 
0.938129484710.2%
 
1.60544931910.2%
 
1.14779198210.2%
 
-1.17252492910.2%
 
0.881507933110.2%
 
-0.397625267510.2%
 
-1.19206905410.2%
 
-0.206627130510.2%
 
-0.383958309910.2%
 
-0.960299253510.2%
 
-1.19599485410.2%
 
-0.586403012310.2%
 
0.143396183810.2%
 
-1.08456838110.2%
 
-0.986677110210.2%
 
-0.709362328110.2%
 
1.28975915910.2%
 
1.19601190110.2%
 
-1.13937437510.2%
 
1.53976929210.2%
 
0.249422237310.2%
 
-1.10033464410.2%
 
-1.45239734610.2%
 
-0.206556111610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.12688875210.2%
 
-2.00954937910.2%
 
-1.88454055810.2%
 
-1.83182680610.2%
 
-1.70689654410.2%
 
-1.59929144410.2%
 
-1.59911680210.2%
 
-1.56138455910.2%
 
-1.5546106110.2%
 
-1.52595984910.2%
 
ValueCountFrequency (%) 
3.72025108310.2%
 
3.16858673110.2%
 
2.82866144210.2%
 
2.80523371710.2%
 
2.79823303210.2%
 
2.78600192110.2%
 
2.73266863810.2%
 
2.6995067610.2%
 
2.59996676410.2%
 
2.47510981610.2%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5370391085743903e-09
Minimum-1.7249253988265991
Maximum1.7244844436645508
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:15.976241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.724925399
5-th percentile-1.555178684
Q1-0.8377525061
median-0.01343056886
Q30.8757668287
95-th percentile1.536603665
Maximum1.724484444
Range3.449409842
Interquartile range (IQR)1.713519335

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)-394160261.9
Kurtosis-1.188611847
Mean-2.537039109e-09
Median Absolute Deviation (MAD)0.876309514
Skewness-0.03359238928
Sum-1.268519554e-06
Variance0.9999999992
2020-08-25T00:42:16.079096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.770497620110.2%
 
-0.78745025410.2%
 
-1.01818609210.2%
 
-0.0855978950910.2%
 
-0.81791055210.2%
 
1.36390376110.2%
 
0.0908598005810.2%
 
1.70180785710.2%
 
-1.13585412510.2%
 
-0.168362736710.2%
 
0.987035751310.2%
 
-0.324381351510.2%
 
-0.193689480410.2%
 
0.607751905910.2%
 
0.144127920310.2%
 
-1.7038079510.2%
 
-0.242472156910.2%
 
-0.864518582810.2%
 
-0.122967943510.2%
 
-1.58939874210.2%
 
-0.152185991410.2%
 
0.83628410110.2%
 
0.728527128710.2%
 
-0.000481933413510.2%
 
-0.726916313210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.72492539910.2%
 
-1.70974564610.2%
 
-1.70797586410.2%
 
-1.70692670310.2%
 
-1.7038079510.2%
 
-1.70059204110.2%
 
-1.69637763510.2%
 
-1.694157610.2%
 
-1.68822264710.2%
 
-1.68508076710.2%
 
ValueCountFrequency (%) 
1.72448444410.2%
 
1.7244533310.2%
 
1.72095835210.2%
 
1.70180785710.2%
 
1.68948662310.2%
 
1.68840444110.2%
 
1.68413758310.2%
 
1.6825158610.2%
 
1.67146778110.2%
 
1.66926062110.2%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.294538378715515e-09
Minimum-1.7146265506744385
Maximum1.6965036392211914
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:16.192528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.714626551
5-th percentile-1.525810105
Q1-0.9020656645
median0.01706929132
Q30.893714413
95-th percentile1.497234172
Maximum1.696503639
Range3.41113019
Interquartile range (IQR)1.795780078

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-772476134
Kurtosis-1.236096431
Mean-1.294538379e-09
Median Absolute Deviation (MAD)0.8944258578
Skewness-0.03425400719
Sum-6.472691894e-07
Variance1.000000004
2020-08-25T00:42:16.297576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.51562237710.2%
 
0.0156357362910.2%
 
0.338049441610.2%
 
0.553053736710.2%
 
-1.04165458710.2%
 
-0.320825219210.2%
 
-0.557943344110.2%
 
-1.52409875410.2%
 
-0.499189108610.2%
 
0.368818491710.2%
 
0.946623265710.2%
 
0.722992122210.2%
 
-0.697132706610.2%
 
-1.48955786210.2%
 
0.807959675810.2%
 
0.721573650810.2%
 
-0.740609049810.2%
 
0.583354294310.2%
 
1.49092924610.2%
 
0.740582525710.2%
 
1.44918692110.2%
 
-1.34877920210.2%
 
1.69212174410.2%
 
-0.906608700810.2%
 
-0.824579417710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.71462655110.2%
 
-1.70447266110.2%
 
-1.70068085210.2%
 
-1.69373595710.2%
 
-1.69179010410.2%
 
-1.68868434410.2%
 
-1.68824803810.2%
 
-1.68295347710.2%
 
-1.6815177210.2%
 
-1.67550110810.2%
 
ValueCountFrequency (%) 
1.69650363910.2%
 
1.69212174410.2%
 
1.68854534610.2%
 
1.67860543710.2%
 
1.67409861110.2%
 
1.67361593210.2%
 
1.67114210110.2%
 
1.66878497610.2%
 
1.66058790710.2%
 
1.65926861810.2%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.92903995513916e-10
Minimum-1.6074162721633911
Maximum1.737965106964111
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:16.413611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.607416272
5-th percentile-1.489978874
Q1-0.8437346518
median-0.03596160375
Q30.8672378659
95-th percentile1.573564416
Maximum1.737965107
Range3.345381379
Interquartile range (IQR)1.710972518

Descriptive statistics

Standard deviation0.999999997
Coefficient of variation (CV)1443201372
Kurtosis-1.233232801
Mean6.929039955e-10
Median Absolute Deviation (MAD)0.8453256786
Skewness0.07969350967
Sum3.464519978e-07
Variance0.999999994
2020-08-25T00:42:16.522534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.935546398210.2%
 
-1.28773474710.2%
 
-0.460107088110.2%
 
-0.519825160510.2%
 
-1.52916145310.2%
 
-1.48573005210.2%
 
-0.43520855910.2%
 
1.42443072810.2%
 
-0.358061343410.2%
 
1.01216363910.2%
 
-0.100379869310.2%
 
-0.231522232310.2%
 
1.53382182110.2%
 
-0.522771298910.2%
 
0.268960863410.2%
 
-0.575168132810.2%
 
1.69989860110.2%
 
-1.56046497810.2%
 
-0.995550274810.2%
 
1.01981389510.2%
 
-0.748369336110.2%
 
1.42838168110.2%
 
-1.39927220310.2%
 
-1.09049856710.2%
 
0.705407977110.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.60741627210.2%
 
-1.60640013210.2%
 
-1.60122382610.2%
 
-1.60005044910.2%
 
-1.59841644810.2%
 
-1.59583663910.2%
 
-1.59452104610.2%
 
-1.59073877310.2%
 
-1.58111381510.2%
 
-1.57756304710.2%
 
ValueCountFrequency (%) 
1.73796510710.2%
 
1.73346066510.2%
 
1.73084819310.2%
 
1.72990739310.2%
 
1.72475814810.2%
 
1.72245442910.2%
 
1.70749926610.2%
 
1.69989860110.2%
 
1.69452154610.2%
 
1.69410204910.2%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.322538971900939e-10
Minimum-1.796939492225647
Maximum1.7736533880233765
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:16.640762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.796939492
5-th percentile-1.602036226
Q1-0.8652615994
median0.03356981976
Q30.8184774965
95-th percentile1.561953789
Maximum1.773653388
Range3.57059288
Interquartile range (IQR)1.683739096

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)1072669156
Kurtosis-1.126624925
Mean9.322538972e-10
Median Absolute Deviation (MAD)0.8361956477
Skewness-0.04422110497
Sum4.661269486e-07
Variance1.000000003
2020-08-25T00:42:16.744614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.59374952310.2%
 
1.4714155210.2%
 
-0.707350373310.2%
 
1.39126455810.2%
 
1.46743810210.2%
 
-1.48059785410.2%
 
0.59700661910.2%
 
-1.07682883710.2%
 
1.49347865610.2%
 
-0.0826001018310.2%
 
0.469865590310.2%
 
-0.538013577510.2%
 
1.0168566710.2%
 
-1.06514918810.2%
 
-0.378106594110.2%
 
-1.1061803110.2%
 
-0.284777730710.2%
 
0.586294949110.2%
 
-1.72142028810.2%
 
-0.343077778810.2%
 
-1.69993984710.2%
 
-0.496764868510.2%
 
0.666383206810.2%
 
-1.4469678410.2%
 
-0.306824773610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.79693949210.2%
 
-1.79502391810.2%
 
-1.79332518610.2%
 
-1.78353965310.2%
 
-1.78099536910.2%
 
-1.73366057910.2%
 
-1.72248756910.2%
 
-1.72142028810.2%
 
-1.70827913310.2%
 
-1.70538330110.2%
 
ValueCountFrequency (%) 
1.77365338810.2%
 
1.7731918110.2%
 
1.75985467410.2%
 
1.74175572410.2%
 
1.73751914510.2%
 
1.72715437410.2%
 
1.71832537710.2%
 
1.69190323410.2%
 
1.68734741210.2%
 
1.68524980510.2%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4742836356163025e-09
Minimum-1.6318912506103516
Maximum1.7889467477798462
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:16.861679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.631891251
5-th percentile-1.493650973
Q1-0.8828013241
median0.04487431794
Q30.8582887203
95-th percentile1.582525235
Maximum1.788946748
Range3.420837998
Interquartile range (IQR)1.741090044

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)-678295531.8
Kurtosis-1.212790178
Mean-1.474283636e-09
Median Absolute Deviation (MAD)0.8749201894
Skewness0.08999134963
Sum-7.371418178e-07
Variance1.000000005
2020-08-25T00:42:16.966963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.528319776110.2%
 
1.66477584810.2%
 
1.37176489810.2%
 
-1.36981260810.2%
 
0.108440697210.2%
 
1.5670876510.2%
 
0.159997776210.2%
 
0.536476075610.2%
 
-0.759061694110.2%
 
-0.425953477610.2%
 
1.67256915610.2%
 
0.600933253810.2%
 
1.34249460710.2%
 
-0.325996190310.2%
 
0.0587989278110.2%
 
0.225187376110.2%
 
-1.2312390810.2%
 
-0.634149432210.2%
 
0.432309538110.2%
 
1.71752357510.2%
 
-0.391419351110.2%
 
-0.586302995710.2%
 
0.404480814910.2%
 
-0.831421971310.2%
 
1.22290563610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.63189125110.2%
 
-1.62298166810.2%
 
-1.62258994610.2%
 
-1.61541545410.2%
 
-1.61397504810.2%
 
-1.61273014510.2%
 
-1.60973358210.2%
 
-1.60864853910.2%
 
-1.60533833510.2%
 
-1.58975863510.2%
 
ValueCountFrequency (%) 
1.78894674810.2%
 
1.78202092610.2%
 
1.77729976210.2%
 
1.77013909810.2%
 
1.7507492310.2%
 
1.74899959610.2%
 
1.74695241510.2%
 
1.74578058710.2%
 
1.74557113610.2%
 
1.73195278610.2%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.155771315097809e-09
Minimum-1.6581813097000122
Maximum1.6924114227294922
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:17.090232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.65818131
5-th percentile-1.50311144
Q1-0.9130574018
median0.001232949086
Q30.868357718
95-th percentile1.563265574
Maximum1.692411423
Range3.350592732
Interquartile range (IQR)1.78141512

Descriptive statistics

Standard deviation0.9999999992
Coefficient of variation (CV)865223064.6
Kurtosis-1.270578304
Mean1.155771315e-09
Median Absolute Deviation (MAD)0.8888241081
Skewness0.007171851333
Sum5.778856575e-07
Variance0.9999999985
2020-08-25T00:42:17.195073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.17988479110.2%
 
-0.0535492226510.2%
 
-0.18659919510.2%
 
-0.831361353410.2%
 
-0.426113724710.2%
 
0.452795952610.2%
 
1.57094740910.2%
 
0.779618918910.2%
 
-1.11197602710.2%
 
-1.12760281610.2%
 
0.17188419410.2%
 
1.11003994910.2%
 
0.961271822510.2%
 
-0.449876010410.2%
 
-0.0985130816710.2%
 
1.25068926810.2%
 
0.415710210810.2%
 
0.826447427310.2%
 
-0.721050202810.2%
 
0.512655317810.2%
 
1.57882547410.2%
 
-1.00265896310.2%
 
1.28000605110.2%
 
-0.864614069510.2%
 
-1.33144938910.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.6581813110.2%
 
-1.64950442310.2%
 
-1.6385173810.2%
 
-1.63182771210.2%
 
-1.6253180510.2%
 
-1.62304925910.2%
 
-1.61893594310.2%
 
-1.60762274310.2%
 
-1.60010778910.2%
 
-1.59192144910.2%
 
ValueCountFrequency (%) 
1.69241142310.2%
 
1.6886758810.2%
 
1.6793558610.2%
 
1.67420995210.2%
 
1.67045474110.2%
 
1.67037296310.2%
 
1.67032837910.2%
 
1.66725909710.2%
 
1.66461050510.2%
 
1.66351783310.2%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.421438694000244e-11
Minimum-1.6724202632904053
Maximum1.7701178789138794
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:17.320381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.672420263
5-th percentile-1.493968362
Q1-0.8840236217
median-0.01259248843
Q30.8375753909
95-th percentile1.586397856
Maximum1.770117879
Range3.442538142
Interquartile range (IQR)1.721599013

Descriptive statistics

Standard deviation0.9999999974
Coefficient of variation (CV)-4.129776236e+10
Kurtosis-1.209914466
Mean-2.421438694e-11
Median Absolute Deviation (MAD)0.8731617033
Skewness0.06852483546
Sum-1.210719347e-08
Variance0.9999999949
2020-08-25T00:42:17.426251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.773436605910.2%
 
-0.788848280910.2%
 
0.355131387710.2%
 
-0.249911665910.2%
 
-1.10216903710.2%
 
-1.33068704610.2%
 
0.761048078510.2%
 
0.381466925110.2%
 
-1.67124497910.2%
 
-0.766917824710.2%
 
-0.743482947310.2%
 
1.52993726710.2%
 
-0.830408692410.2%
 
1.69009661710.2%
 
1.34635031210.2%
 
-1.27800524210.2%
 
1.09058475510.2%
 
-1.34636855110.2%
 
0.0421350859110.2%
 
-0.953461110610.2%
 
1.75850367510.2%
 
-0.721051096910.2%
 
0.145839586910.2%
 
-0.698595404610.2%
 
0.00327821145810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.67242026310.2%
 
-1.67124497910.2%
 
-1.66940748710.2%
 
-1.65436422810.2%
 
-1.65310549710.2%
 
-1.6458700910.2%
 
-1.64077675310.2%
 
-1.63868498810.2%
 
-1.63076543810.2%
 
-1.62882816810.2%
 
ValueCountFrequency (%) 
1.77011787910.2%
 
1.76480400610.2%
 
1.75850367510.2%
 
1.73795115910.2%
 
1.72869825410.2%
 
1.72594094310.2%
 
1.7195295110.2%
 
1.71455812510.2%
 
1.70979225610.2%
 
1.7038818610.2%
 

target
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.568167686462402e-11
Minimum-2.4797208309173584
Maximum2.4489459991455083
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:42:17.542485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.479720831
5-th percentile-1.896728247
Q1-0.5873515159
median0.2221572176
Q30.7709275037
95-th percentile1.29526881
Maximum2.448945999
Range4.92866683
Interquartile range (IQR)1.35827902

Descriptive statistics

Standard deviation0.9999999968
Coefficient of variation (CV)1.167110675e+10
Kurtosis-0.4972432408
Mean8.568167686e-11
Median Absolute Deviation (MAD)0.6210860983
Skewness-0.6005201001
Sum4.284083843e-08
Variance0.9999999936
2020-08-25T00:42:17.642327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.35937762310.2%
 
0.922514200210.2%
 
0.883127272110.2%
 
-0.547193646410.2%
 
1.18814015410.2%
 
1.36392259610.2%
 
-0.77473586810.2%
 
-0.214814275510.2%
 
0.0913902521110.2%
 
-0.438230991410.2%
 
1.30533957510.2%
 
0.257940858610.2%
 
0.834314763510.2%
 
1.13410270210.2%
 
1.44793105110.2%
 
0.602670371510.2%
 
-0.150482341610.2%
 
-0.299487501410.2%
 
0.827935278410.2%
 
-1.12959504110.2%
 
-0.742533206910.2%
 
-1.42257285110.2%
 
0.0445164814610.2%
 
-1.17753148110.2%
 
-2.21235394510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.47972083110.2%
 
-2.38594913510.2%
 
-2.37754726410.2%
 
-2.36453151710.2%
 
-2.34649610510.2%
 
-2.3113527310.2%
 
-2.29475760510.2%
 
-2.29315733910.2%
 
-2.21235394510.2%
 
-2.17958474210.2%
 
ValueCountFrequency (%) 
2.44894599910.2%
 
1.82102739810.2%
 
1.73595285410.2%
 
1.64720213410.2%
 
1.59450948210.2%
 
1.58263814410.2%
 
1.56062793710.2%
 
1.51517033610.2%
 
1.51073837310.2%
 
1.50087654610.2%
 

Interactions

2020-08-25T00:41:57.393625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:57.511941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:57.637486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:57.762333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:57.888971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.016643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.144148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.270539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.397238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.524637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.651634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.771167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:58.901624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.039076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.334599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.472648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.612430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.749667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:41:59.893960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.031703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.171014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.307644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.437060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.564456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.699865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.835240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:00.972037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.110523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.249402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.387778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.525489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.665408image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.806108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:01.937436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.080656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.219193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.354290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.492505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.635479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.773314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:02.921518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.060026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.197519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.500722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.634274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.767429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:03.912193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.051923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.192703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.331388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.469591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.609191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.755686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:04.899200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.037827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.171073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.306436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.448830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.590615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.732798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:05.880645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.023280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.164467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.303957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.444052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.583053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.716122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.845268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:06.991369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:07.129833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:07.272643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:07.414645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:07.722140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:07.866425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.004551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.141998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.281033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.409637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.550765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.693915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.832859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:08.972032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.110900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.253382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.391210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.528589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.666976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.811878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:09.943274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.076475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.215388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.354900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.494299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.636496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.775146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:10.914840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.054533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.194161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.334076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.466308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.597957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:11.904571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.050372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.191132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.333574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.474021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.632427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.774506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:12.914222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.053814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.184514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.304780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.432154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.558283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.698797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.833352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:13.964053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:14.102970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:14.232257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:14.365561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:14.497098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:42:17.764629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:42:17.987725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:42:18.205541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:42:18.428619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:42:14.730267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:42:15.003917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.1836000.4459630.032726-0.1720180.479583-1.323086-0.1299770.475801-0.171670-1.566382-0.494488
10.136704-0.204772-0.593439-0.198238-0.309773-1.481634-0.872522-0.7973790.691568-1.6288280.755893
20.7434680.248154-0.312359-0.299351-1.1122090.9664641.0430200.810568-0.318813-1.453131-0.915571
30.370380-0.047819-0.383488-1.3303991.212017-1.0315370.603254-1.224464-0.2906401.1517490.247545
40.9747700.7316101.6054491.4097020.2306001.6489410.130976-0.5255440.8340351.512775-1.325878
5-1.577893-0.993207-0.638903-1.057047-0.706559-0.186741-0.843504-1.4074001.0193560.145613-0.323179
6-0.786985-0.397093-0.6556550.1771430.4536071.7074991.4674381.725094-1.586748-1.5291411.105159
7-1.300091-0.582917-1.301248-1.505004-0.0640600.047244-0.174560-0.5069980.3754170.7696540.271399
80.498100-0.399513-0.055677-1.593334-1.488130-0.4593880.4011620.184316-0.0108220.306370-0.143162
9-1.506607-1.511531-0.4342830.166500-0.8245791.2571590.8878561.5674440.6365541.703882-0.364846

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
4901.5776971.6423870.4531980.0442610.412094-0.5751680.597007-1.2831081.208142-0.7432470.693340
4911.7045391.5648872.2683571.614408-0.779843-0.6974290.275108-0.8971220.066375-0.9534611.155791
4921.1953071.369803-1.256302-1.5071000.435002-0.839697-0.874275-1.493250-1.4055540.925885-0.756625
493-0.6975870.0551360.3452400.0514131.4599350.939826-0.035277-0.926293-0.669618-0.0055041.008177
494-0.0110120.0335560.6635141.3683660.067145-1.505043-1.351102-0.6320151.570947-1.2776371.105118
4951.8089031.0683100.859387-0.1834660.723207-0.037413-1.210552-0.510398-1.1575121.051495-0.356477
4961.1127891.3483180.344472-1.584316-1.3262041.535183-1.0497221.6846800.5849960.541695-2.074130
497-0.3118860.4392901.5135491.2129140.4370821.484049-0.220436-0.7285511.459280-0.8741840.834315
498-1.296177-0.763492-0.3435461.236215-1.056458-0.158714-0.3038701.788947-1.5500450.4679600.602670
4990.5707060.7925830.8456460.7187391.580176-0.4717100.004920-0.8063940.6896761.714558-1.199751